Fuzzing the Aleo VM: Two Approaches to Bulletproof Security
When you're building a virtual machine that will handle real value and execute privacy-critical computations, "it works on my machine" isn't good enough. Every edge case, malformed input, and unexpected program structure needs to be handled gracefully. That's why we've implemented comprehensive fuzzing for the Aleo VM using two complementary approaches.
What is Fuzzing?
Fuzzing is an automated testing technique that feeds random or semi-random data to a program to discover bugs, crashes, and security vulnerabilities. It's become standard practice across the tech industry—Google, Apple, Microsoft, and virtually every major software company use fuzzing to harden their systems before release. Rather than relying solely on human testers to think of edge cases, fuzzing systematically explores the unexpected: malformed inputs, boundary conditions, and combinations that would never occur in normal usage but could be exploited by attackers.
Why Fuzz a Virtual Machine?
Virtual machines are complex systems with numerous attack surfaces. In order for Aleo to be adopted by major traditional infrastructure, it needs enterprise-grade reliability. Traditional testing covers expected use cases, but fuzzing discovers the unexpected ones: the malformed programs, edge case inputs, and combinations that no human tester would think to try.
For Aleo, this is particularly critical. A bug doesn't just hurt some proof-of-concept code—the Aleo network is a production-ready system securing billions of dollars of value. Our VM executes zero-knowledge programs that handle sensitive data and financial transactions.
Approach 1: Structure-Aware Fuzzing - Testing with Valid Inputs
Our first approach uses structure-aware fuzzing with AFL (American Fuzzy Lop) combined with Rust's arbitrary crate. Instead of feeding random bytes to the VM, this method generates semantically valid program structures and inputs.
The Architecture
// Fuzz target that generates structured inputs
afl::fuzz_nohook!(|structured_input: StructuredInputType| {
// Test critical VM operations
// Examples: Process::add_program + Process::authorize
});The arbitrary crate allows us to define how random bytes should be interpreted as valid Aleo program components—identifiers, instructions, function calls, and data structures. This ensures the fuzzer spends time testing the VM's logic rather than just input validation.
Seed Generation
One challenge with structure-aware fuzzing is creating meaningful starting seeds. We built a dedicated tool that reverse-engineers valid seeds:
fn main() {
let mut rng = TestRng::default();
let mut buf = [0u8; PREDICTED_SEED_SIZE];
loop {
buf.try_fill(&mut rng).unwrap();
let mut u = Unstructured::new(&buf);
let seed = StructuredInputType::arbitrary(&mut u).unwrap();
if seed_is_valid(&seed) {
std::fs::write(SEED_PATH, &buf).unwrap();
break;
}
}
}This approach generates high-quality seeds that give AFL a strong foundation for mutation and discovery.
Approach 2: Grammar-Based Fuzzing - Testing the Full Pipeline
Our second approach uses grammar-based fuzzing, which generates syntactically valid Aleo programs from ABNF (Augmented Backus-Naur Form) grammar specifications. This tests the entire pipeline from parsing through execution.
Flexible Feature Sets
We've implemented multiple fuzzing profiles for different testing scenarios:
Parsing-only: Tests program parsing and validation
Deploy: Adds program deployment logic
Verify deployment: Includes deployment verification
Authorize: Tests function authorization mechanisms
Execute: Full program execution testing
Full: Complete end-to-end pipeline testing
Each profile balances depth versus breadth, deeper testing finds complex interaction bugs, while broader testing covers more surface area. If you’re interested to know how the above concepts tie together, you may be interested in the following overview of Aleo’s transaction lifecycle: https://developer.aleo.org/concepts/fundamentals/transactions#transaction-lifecycle.
Grammar Flexibility
The grammar-based approach allows us to create specialized test scenarios. We can focus on specific instruction types, test parsing edge cases, or generate programs with particular structural characteristics. This flexibility means we can adapt our fuzzing strategy as new features are added to the VM. If you’re interested in Aleo’s language formal grammars, you can find them here: https://github.com/ProvableHQ/grammars.
Real Vulnerabilities Discovered
Our fuzzing campaigns have uncovered several fortifications, for example:
Arithmetic Overflow in Debug-mode Field Operations
We discovered that Fp256::mul_assign (and Fp384) used a potentially overflowing addition for carry operations. In debug builds, this caused panics that could be triggered by specific field element combinations. While release builds wrapped around safely, our tests could be improved by avoiding the divergent behavior.
Performance Bottlenecks
Fuzzing revealed that certain hash operations (hash.ped64, hash.ped128) and global constant initialization were significantly slower than expected.
Tooling for the Community
Beyond finding bugs in our own code, we've built reusable tools that benefit the broader ecosystem (you can find a link at the bottom of the article):
corpus_processor: Intelligent Test Case Filtering
Our corpus processor does deduplication. It parses each fuzzer-generated program, validates it can be added to the Aleo VM, and tests function authorization. The tool uses normalized Damerau-Levenshtein distance to reject programs that are 90%+ similar to existing ones, ensuring each saved test case adds meaningful coverage.
// Skip entries that are barely different from saved ones
if strsim::normalized_damerau_levenshtein(&corpus_string, seed) > 0.9 {
anyhow::bail!("Copycat input");
}crash_processor: Systematic Vulnerability Analysis
The crash processor runs fuzzer outputs through the VM pipeline and categorizes failures by panic location. It filters out noise (single occurrences) and sorts issues by frequency, making it easy to identify the most critical bugs first.
abnf_converter: Grammar-to-JSON Pipeline
This tool converts ABNF grammar specifications into JSON format for fuzzing frameworks. It handles complex grammar features like nested groups, repetitions with variable bounds, and terminal value ranges. The converter enables rapid prototyping of specialized fuzzing grammars for different Aleo language features.
// Convert ABNF repetition rules to meaningful JSON rule names
fn repetition_rule_name(node: &Node, toplevel: bool) -> String {
match repeat {
Repeat::Variable { min, max } => {
if let Some(min) = min {
format!("at-least-{min}")
} else if let Some(max) = max {
format!("at-most-{max}")
} else {
format!("zero-or-more")
}
}
}
}Lessons Learned
Complementary Approaches Work Best
Structure-aware fuzzing excels at testing post-parsing logic with valid program structures, while grammar-based fuzzing is superior for comprehensive parsing and syntax edge case discovery. Using both approaches provides better coverage than either alone.
Performance Matters for Fuzzing
Slow fuzzing targets discover fewer bugs per unit time. We've learned to optimize our fuzzing setup:
Use release builds when possible
Set appropriate timeout values (
-tparameter)Restrict input size ranges (
-gand-Gparameters) for focused testing
Meaningful Seeds Accelerate Discovery
Random fuzzing input generation is ineffective. High-quality seed programs from our existing test suite provide much better starting points for mutation-based discovery.
The Ongoing Campaign
Fuzzing isn't a one-time activity—it's an ongoing security practice. We continuously run fuzzing campaigns as new features are added to the VM. Each major release includes fuzzing validation, and our CI pipeline incorporates lightweight fuzzing to catch regressions.
The dual approach—structure-aware and grammar-based—ensures we're testing both the happy path with valid inputs and the edge cases with malformed or unexpected programs. This comprehensive testing strategy gives us confidence that the Aleo VM can handle whatever the real world throws at it.
For Developers
If you're building applications on Aleo or contributing to the protocol, consider how fuzzing can improve your own code quality. The tools we've built are open source and can be adapted for testing Aleo programs, smart contracts, or any system that processes structured inputs.
Security isn't just about cryptographic correctness—it's about robust implementation of every component in the system. Fuzzing helps us find the gaps that code review and traditional testing miss.
The fuzzing tools and documentation are available at github.com/ProvableHQ/afl_program_tools. We welcome contributions and feedback from the community.